BENEFITS:Accelerated Product Development
CycleOptimisation of Experimental Design
using Model Based DoEEarly stage assessment of
manufacturing robustnessIncreased process understandingUnderstand Product and Process
Sensitivities, de-risking capital investments
DIGITALISATION OFPHARMACEUTICAL PRODUCTDEVELOPMENTWith the Industry 4.0 initiative drivingdigitalisation in many manufacturingsectors, there is significant progress in thedevelopment of optimised scale-up, andtech transfer tools to assist with efficiencyimprovements required in PharmaceuticalProduct Development to lower the cost ofearly stage development.
THE NEXT STEP IN PHARMACEUTICALMANUFACTURINGThere are a number of obstacles toconsistent and efficient productdevelopment and technical transfer tomanufacturing in the pharmaceuticalsector. Despite considerable research, andevidence that greatest benefits areachieved with ’model-based drugdevelopment’, when models are fullyintegrated in the process, there remainsome challenges to deploy models withinmanufacturing.
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A Workshop for improving thedesign, development and
transfer of formulated productsinto manufacturing using the“Digital Twin” methodology
DIGITAL TWINModel-based engineering tools to identify an optimised route forpharmaceutical drug development are in regular use, but achievingtechnology transfer into the manufacturing environment has remained achallenge. Through a collaboration by PSE and Perceptive Engineeringthere is now an integrated platform for Product Development andManufacturing. This overcomes the difficulties of transference of modelsand knowledge, enabling a complete control system to be generatedfrom mechanistic process development models.
The Digital Twin workshop comprises a two-day hands on activity,introducing the tools and skills for development of mechanistic models,optimised DoE’s, Model based control and multivariate monitoringtechniques to deploy on a GxP ready platform. Using leading-edgesoftware, delegates will learn the how the “Digital Twin” is constructed,explore, through sensitivity analysis, the impact of raw material variabilityand determine when and how a feedback/feedforward model predictivecontrol system can improve product quality and optimise throughput.
THE WORKSHOP OBJECTIVES
g FORMULATE
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Perceptive Engineering LimitedVanguard House, Daresbury Science and Innovation Campus, Keckwick Lane, Daresbury, Cheshire WA4 4AB, UK.t. +44 (0)1925 607150 e. [email protected]. +44 (0)1925 607161 w.www.perceptiveapc.com
Model based engineering tools to achievean optimised route for Pharmaceutical
Drug development.
PERCEPTIVE ENGINEERING LTD works with some of the most innovative companies, including Pfizer, Abbott,GSK, Merck, Takeda and many others as partners in designing, developingand deploying Advanced Process Control strategies. In collaboration withProcess Systems Enterprise (PSE) we are able to provide a comprehensive“Development to Manufacturing” environment for formulated products.
As both companies are partners in the Advanced Digital Design ofPharmaceutical Therapeutics (ADDOPT TM) we are at the forefront ofdevelopments in the digitalisation of the Pharmaceutical Industry. Through thisworkshop we aim to drive the Industry 4.0 concept of the “Digital Twin” tocontribute toward the future of pharmaceutical manufacturing.
The Perceptive/PSE “Digital Twin” Workshop
IntroductionWorkshop Objectives, Introduce the “Digital Twin”
Introduction to gPROMS Formulated Products
gCRYSTAL modules o Overview of capabilities o Common applications o Batch & continuous processes
Mechanistic model – Simulation &Configuration
Case Study – Batch cooling crystallizationPlant crystallization process
o Getting started o Simulating the plant process
Model validation o Scale-down o Entering experimental data o Solubility verification o Setting up parameter estimation o Running a parameter estimation o Model verification and applicability
Model deployment options o Scale-up – validation at plant scale o Optimisation of plant recipe o Simulating the optimal point
Additional platform capabilitiesGlobal System Analysis (GSA)
o Parametric Studies o Uncertainty analyses o Sensitivity analyses
Application case – Batch cooling crystallization
Data integrationData import tool – easier input of experimental data
Introduction to Advanced Process Control techniques
Define Models for calibration, monitoring, control and Optimisation
Inferential, (“Soft” sensor) applicationsWhite, Black and Grey Box modellingModel Predictive Control; what, why, when, where!
Introduction to APC for CrystallisationSolution overviewTrajectory following Model Predictive Control
for supersaturation controlParticle size control using FBRM
Simulating gCRYSTAL within PerceptiveAPCCase Study – CSTR crystallizationOverview of the integration of gCRYSTAL model
and PerceptiveAPC Process Response TestsProcess response tests to build a MPC for
CSTR temperature controlProcess response tests for Metastable zone generationDeveloping the Supersaturation model
MPC ConfigurationConfiguration of the Trajectory following
temperature controllerConfiguration of the MPC for Supersaturation control Simulation using gCRYSTAL with PerceptiveAPC to assess Operational PerformanceRunning the integrated solution to explore
operational scenarios.
